Overview

Dataset statistics

Number of variables22
Number of observations2938
Missing cells2563
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory505.1 KiB
Average record size in memory176.0 B

Variable types

Numeric20
Categorical2

Dataset

DescriptionThe life expectancy data-set contains health factors for 193 countries for the years 2000-2015 has been collected from a WHO data repository website and its corresponding economic data was collected from United Nation website. The dataset consists of 22 Columns and 2938 rows which meant 20 predicting variables
CreatorArchimedes
AuthorEratosthenes
URLhttps://www.kaggle.com/datasets/kumarajarshi/life-expectancy-who
Copyright(c) Euclid Pythagorus

Alerts

Country has a high cardinality: 193 distinct valuesHigh cardinality
infant deaths is highly overall correlated with under-five deaths High correlation
under-five deaths is highly overall correlated with infant deathsHigh correlation
thinness 1-19 years is highly overall correlated with thinness 5-9 yearsHigh correlation
thinness 5-9 years is highly overall correlated with thinness 1-19 yearsHigh correlation
Polio is highly overall correlated with Diphtheria High correlation
Diphtheria is highly overall correlated with PolioHigh correlation
Income composition of resources is highly overall correlated with SchoolingHigh correlation
Schooling is highly overall correlated with Income composition of resourcesHigh correlation
Hepatitis B is highly overall correlated with Diphtheria High correlation
BMI has 34 (1.2%) missing valuesMissing
thinness 1-19 years has 34 (1.2%) missing valuesMissing
thinness 5-9 years has 34 (1.2%) missing valuesMissing
Alcohol has 194 (6.6%) missing valuesMissing
GDP has 448 (15.2%) missing valuesMissing
Hepatitis B has 553 (18.8%) missing valuesMissing
Income composition of resources has 167 (5.7%) missing valuesMissing
Population has 652 (22.2%) missing valuesMissing
Schooling has 163 (5.5%) missing valuesMissing
Total expenditure has 226 (7.7%) missing valuesMissing
Country is uniformly distributedUniform
Income composition of resources has 130 (4.4%) zerosZeros
infant deaths has 848 (28.9%) zerosZeros
Measles has 983 (33.5%) zerosZeros
percentage expenditure has 611 (20.8%) zerosZeros
under-five deaths has 785 (26.7%) zerosZeros

Reproduction

Analysis started2023-01-29 18:21:19.856570
Analysis finished2023-01-29 18:22:04.269513
Duration44.41 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

BMI
Real number (ℝ)

Distinct608
Distinct (%)20.9%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean38.32124656
Minimum1
Maximum87.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:04.349551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.2
Q119.3
median43.5
Q356.2
95-th percentile64.785
Maximum87.3
Range86.3
Interquartile range (IQR)36.9

Descriptive statistics

Standard deviation20.0440335
Coefficient of variation (CV)0.5230527528
Kurtosis-1.291095468
Mean38.32124656
Median Absolute Deviation (MAD)16.3
Skewness-0.2193116034
Sum111284.9
Variance401.7632791
MonotonicityNot monotonic
2023-01-29T13:22:04.468308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.5 18
 
0.6%
55.8 16
 
0.5%
57 16
 
0.5%
54.2 15
 
0.5%
59.9 15
 
0.5%
59.3 14
 
0.5%
52.8 13
 
0.4%
55 13
 
0.4%
59.4 13
 
0.4%
56.5 13
 
0.4%
Other values (598) 2758
93.9%
(Missing) 34
 
1.2%
ValueCountFrequency (%)
1 1
< 0.1%
1.4 2
0.1%
1.8 1
< 0.1%
1.9 1
< 0.1%
2 1
< 0.1%
ValueCountFrequency (%)
87.3 1
< 0.1%
83.3 1
< 0.1%
82.8 1
< 0.1%
81.6 1
< 0.1%
79.3 1
< 0.1%

HIV/AIDS
Real number (ℝ)

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.742103472
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:04.590656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile8.515
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.077784531
Coefficient of variation (CV)2.914743363
Kurtosis34.89200787
Mean1.742103472
Median Absolute Deviation (MAD)0
Skewness5.396112042
Sum5118.3
Variance25.78389574
MonotonicityNot monotonic
2023-01-29T13:22:04.713439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 1781
60.6%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.9 32
 
1.1%
0.8 32
 
1.1%
0.7 29
 
1.0%
1.5 21
 
0.7%
Other values (190) 658
 
22.4%
ValueCountFrequency (%)
0.1 1781
60.6%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
ValueCountFrequency (%)
50.6 1
< 0.1%
50.3 1
< 0.1%
49.9 1
< 0.1%
49.1 1
< 0.1%
48.8 1
< 0.1%

thinness 1-19 years
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct200
Distinct (%)6.9%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.839703857
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:04.848537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.3
Q37.2
95-th percentile13.8
Maximum27.7
Range27.6
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.420194947
Coefficient of variation (CV)0.9133193018
Kurtosis3.97043867
Mean4.839703857
Median Absolute Deviation (MAD)2.3
Skewness1.711471088
Sum14054.5
Variance19.53812337
MonotonicityNot monotonic
2023-01-29T13:22:05.026397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 74
 
2.5%
1.9 65
 
2.2%
0.8 64
 
2.2%
0.7 63
 
2.1%
1.2 62
 
2.1%
2.1 61
 
2.1%
1.5 60
 
2.0%
2.2 58
 
2.0%
0.9 57
 
1.9%
2 57
 
1.9%
Other values (190) 2283
77.7%
ValueCountFrequency (%)
0.1 28
1.0%
0.2 40
1.4%
0.3 32
1.1%
0.4 5
 
0.2%
0.5 35
1.2%
ValueCountFrequency (%)
27.7 1
< 0.1%
27.5 1
< 0.1%
27.4 1
< 0.1%
27.3 1
< 0.1%
27.2 2
0.1%

thinness 5-9 years
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct207
Distinct (%)7.1%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.870316804
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:05.215782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.5
median3.3
Q37.2
95-th percentile13.8
Maximum28.6
Range28.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.508882087
Coefficient of variation (CV)0.9257882532
Kurtosis4.358730342
Mean4.870316804
Median Absolute Deviation (MAD)2.3
Skewness1.777423977
Sum14143.4
Variance20.33001767
MonotonicityNot monotonic
2023-01-29T13:22:05.400768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 69
 
2.3%
1.1 67
 
2.3%
0.5 63
 
2.1%
1.9 63
 
2.1%
1 62
 
2.1%
2.1 61
 
2.1%
1.3 59
 
2.0%
1.5 57
 
1.9%
1.7 55
 
1.9%
0.6 54
 
1.8%
Other values (197) 2294
78.1%
ValueCountFrequency (%)
0.1 37
1.3%
0.2 45
1.5%
0.3 25
 
0.9%
0.4 17
 
0.6%
0.5 63
2.1%
ValueCountFrequency (%)
28.6 1
< 0.1%
28.5 1
< 0.1%
28.4 1
< 0.1%
28.3 1
< 0.1%
28.2 1
< 0.1%

Adult Mortality
Real number (ℝ)

Distinct425
Distinct (%)14.5%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean164.7964481
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:05.599549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3228
95-th percentile398.3
Maximum723
Range722
Interquartile range (IQR)154

Descriptive statistics

Standard deviation124.292079
Coefficient of variation (CV)0.754215764
Kurtosis1.748860208
Mean164.7964481
Median Absolute Deviation (MAD)76
Skewness1.174369488
Sum482524
Variance15448.5209
MonotonicityNot monotonic
2023-01-29T13:22:05.779023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 34
 
1.2%
14 30
 
1.0%
16 29
 
1.0%
11 25
 
0.9%
138 25
 
0.9%
19 23
 
0.8%
144 22
 
0.7%
15 21
 
0.7%
17 21
 
0.7%
13 21
 
0.7%
Other values (415) 2677
91.1%
ValueCountFrequency (%)
1 12
0.4%
2 8
0.3%
3 6
0.2%
4 4
 
0.1%
5 2
 
0.1%
ValueCountFrequency (%)
723 1
< 0.1%
717 1
< 0.1%
715 1
< 0.1%
699 1
< 0.1%
693 1
< 0.1%

Alcohol
Real number (ℝ)

Distinct1076
Distinct (%)39.2%
Missing194
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean4.602860787
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:05.913011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.8775
median3.755
Q37.7025
95-th percentile11.96
Maximum17.87
Range17.86
Interquartile range (IQR)6.825

Descriptive statistics

Standard deviation4.052412659
Coefficient of variation (CV)0.8804117366
Kurtosis-0.8029092244
Mean4.602860787
Median Absolute Deviation (MAD)3.245
Skewness0.5895625281
Sum12630.25
Variance16.42204836
MonotonicityNot monotonic
2023-01-29T13:22:06.028804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 288
 
9.8%
0.03 15
 
0.5%
0.04 13
 
0.4%
0.02 12
 
0.4%
0.09 12
 
0.4%
0.21 10
 
0.3%
0.06 10
 
0.3%
1.18 10
 
0.3%
0.05 9
 
0.3%
0.49 9
 
0.3%
Other values (1066) 2356
80.2%
(Missing) 194
 
6.6%
ValueCountFrequency (%)
0.01 288
9.8%
0.02 12
 
0.4%
0.03 15
 
0.5%
0.04 13
 
0.4%
0.05 9
 
0.3%
ValueCountFrequency (%)
17.87 1
< 0.1%
17.31 1
< 0.1%
16.99 1
< 0.1%
16.58 1
< 0.1%
16.35 1
< 0.1%

Country
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct193
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Afghanistan
 
16
Peru
 
16
Nicaragua
 
16
Niger
 
16
Nigeria
 
16
Other values (188)
2858 

Length

Max length52
Median length34
Mean length10.04118448
Min length4

Characters and Unicode

Total characters29501
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.3%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan 16
 
0.5%
Peru 16
 
0.5%
Nicaragua 16
 
0.5%
Niger 16
 
0.5%
Nigeria 16
 
0.5%
Norway 16
 
0.5%
Oman 16
 
0.5%
Pakistan 16
 
0.5%
Panama 16
 
0.5%
Papua New Guinea 16
 
0.5%
Other values (183) 2778
94.6%

Length

2023-01-29T13:22:06.150000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic 192
 
4.5%
of 192
 
4.5%
and 97
 
2.3%
united 64
 
1.5%
democratic 48
 
1.1%
the 48
 
1.1%
guinea 48
 
1.1%
saint 33
 
0.8%
ireland 32
 
0.7%
congo 32
 
0.7%
Other values (223) 3502
81.7%

Most occurring characters

ValueCountFrequency (%)
a 4190
 
14.2%
i 2535
 
8.6%
e 2178
 
7.4%
n 2104
 
7.1%
o 1638
 
5.6%
r 1635
 
5.5%
1350
 
4.6%
u 1126
 
3.8%
l 1110
 
3.8%
t 1107
 
3.8%
Other values (46) 10528
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23976
81.3%
Uppercase Letter 3967
 
13.4%
Space Separator 1350
 
4.6%
Open Punctuation 64
 
0.2%
Close Punctuation 64
 
0.2%
Other Punctuation 48
 
0.2%
Dash Punctuation 32
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4190
17.5%
i 2535
10.6%
e 2178
 
9.1%
n 2104
 
8.8%
o 1638
 
6.8%
r 1635
 
6.8%
u 1126
 
4.7%
l 1110
 
4.6%
t 1107
 
4.6%
d 867
 
3.6%
Other values (17) 5486
22.9%
Uppercase Letter
ValueCountFrequency (%)
S 466
 
11.7%
B 336
 
8.5%
C 289
 
7.3%
M 275
 
6.9%
A 256
 
6.5%
G 240
 
6.0%
R 240
 
6.0%
T 209
 
5.3%
I 194
 
4.9%
P 193
 
4.9%
Other values (14) 1269
32.0%
Space Separator
ValueCountFrequency (%)
1350
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Other Punctuation
ValueCountFrequency (%)
' 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27943
94.7%
Common 1558
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4190
15.0%
i 2535
 
9.1%
e 2178
 
7.8%
n 2104
 
7.5%
o 1638
 
5.9%
r 1635
 
5.9%
u 1126
 
4.0%
l 1110
 
4.0%
t 1107
 
4.0%
d 867
 
3.1%
Other values (41) 9453
33.8%
Common
ValueCountFrequency (%)
1350
86.6%
( 64
 
4.1%
) 64
 
4.1%
' 48
 
3.1%
- 32
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29485
99.9%
None 16
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4190
 
14.2%
i 2535
 
8.6%
e 2178
 
7.4%
n 2104
 
7.1%
o 1638
 
5.6%
r 1635
 
5.5%
1350
 
4.6%
u 1126
 
3.8%
l 1110
 
3.8%
t 1107
 
3.8%
Other values (45) 10512
35.7%
None
ValueCountFrequency (%)
ô 16
100.0%

Diphtheria
Real number (ℝ)

Distinct81
Distinct (%)2.8%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.32408359
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:06.266177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.71691207
Coefficient of variation (CV)0.2880920265
Kurtosis3.558143
Mean82.32408359
Median Absolute Deviation (MAD)6
Skewness-2.072752929
Sum240304
Variance562.4919181
MonotonicityNot monotonic
2023-01-29T13:22:06.388315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 350
 
11.9%
98 254
 
8.6%
97 205
 
7.0%
96 201
 
6.8%
95 200
 
6.8%
94 149
 
5.1%
93 120
 
4.1%
92 100
 
3.4%
91 91
 
3.1%
89 76
 
2.6%
Other values (71) 1173
39.9%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 4
 
0.1%
4 12
0.4%
5 10
0.3%
6 16
0.5%
ValueCountFrequency (%)
99 350
11.9%
98 254
8.6%
97 205
7.0%
96 201
6.8%
95 200
6.8%

GDP
Real number (ℝ)

Distinct2490
Distinct (%)100.0%
Missing448
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean7483.158469
Minimum1.68135
Maximum119172.7418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:06.508909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.68135
5-th percentile68.05001537
Q1463.935626
median1766.947595
Q35910.806335
95-th percentile41606.84833
Maximum119172.7418
Range119171.0605
Interquartile range (IQR)5446.870709

Descriptive statistics

Standard deviation14270.16934
Coefficient of variation (CV)1.906971421
Kurtosis12.33307364
Mean7483.158469
Median Absolute Deviation (MAD)1592.456071
Skewness3.20665487
Sum18633064.59
Variance203637733
MonotonicityNot monotonic
2023-01-29T13:22:06.637088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
584.25921 1
 
< 0.1%
354.8185998 1
 
< 0.1%
358.99731 1
 
< 0.1%
43.646498 1
 
< 0.1%
416.14838 1
 
< 0.1%
391.515524 1
 
< 0.1%
375.5819866 1
 
< 0.1%
348.151511 1
 
< 0.1%
341.2894618 1
 
< 0.1%
292.55962 1
 
< 0.1%
Other values (2480) 2480
84.4%
(Missing) 448
 
15.2%
ValueCountFrequency (%)
1.68135 1
< 0.1%
3.685949 1
< 0.1%
4.6135745 1
< 0.1%
5.6687264 1
< 0.1%
8.376432 1
< 0.1%
ValueCountFrequency (%)
119172.7418 1
< 0.1%
115761.577 1
< 0.1%
114293.8433 1
< 0.1%
113751.85 1
< 0.1%
89739.7117 1
< 0.1%

Hepatitis B
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)3.6%
Missing553
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean80.94046122
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:06.761945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q177
median92
Q397
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.07001559
Coefficient of variation (CV)0.3097340343
Kurtosis2.770259399
Mean80.94046122
Median Absolute Deviation (MAD)6
Skewness-1.930845104
Sum193043
Variance628.5056818
MonotonicityNot monotonic
2023-01-29T13:22:06.883095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 240
 
8.2%
98 210
 
7.1%
96 167
 
5.7%
97 155
 
5.3%
95 149
 
5.1%
94 127
 
4.3%
93 101
 
3.4%
92 92
 
3.1%
91 75
 
2.6%
89 71
 
2.4%
Other values (77) 998
34.0%
(Missing) 553
18.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 4
 
0.1%
4 4
 
0.1%
5 9
0.3%
6 17
0.6%
ValueCountFrequency (%)
99 240
8.2%
98 210
7.1%
97 155
5.3%
96 167
5.7%
95 149
5.1%

Income composition of resources
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct625
Distinct (%)22.6%
Missing167
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.6275510646
Minimum0
Maximum0.948
Zeros130
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:07.003757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.277
Q10.493
median0.677
Q30.779
95-th percentile0.892
Maximum0.948
Range0.948
Interquartile range (IQR)0.286

Descriptive statistics

Standard deviation0.2109035552
Coefficient of variation (CV)0.3360739341
Kurtosis1.392814239
Mean0.6275510646
Median Absolute Deviation (MAD)0.127
Skewness-1.14376272
Sum1738.944
Variance0.04448030958
MonotonicityNot monotonic
2023-01-29T13:22:07.129067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
 
4.4%
0.7 17
 
0.6%
0.739 13
 
0.4%
0.714 12
 
0.4%
0.636 12
 
0.4%
0.737 11
 
0.4%
0.734 11
 
0.4%
0.797 11
 
0.4%
0.86 11
 
0.4%
0.703 11
 
0.4%
Other values (615) 2532
86.2%
(Missing) 167
 
5.7%
ValueCountFrequency (%)
0 130
4.4%
0.253 1
 
< 0.1%
0.255 1
 
< 0.1%
0.261 1
 
< 0.1%
0.266 1
 
< 0.1%
ValueCountFrequency (%)
0.948 1
< 0.1%
0.945 1
< 0.1%
0.942 1
< 0.1%
0.941 1
< 0.1%
0.939 1
< 0.1%

infant deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.30394826
Minimum0
Maximum1800
Zeros848
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:07.253015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q322
95-th percentile94.15
Maximum1800
Range1800
Interquartile range (IQR)22

Descriptive statistics

Standard deviation117.9265013
Coefficient of variation (CV)3.891456661
Kurtosis116.0427561
Mean30.30394826
Median Absolute Deviation (MAD)3
Skewness9.78696295
Sum89033
Variance13906.65971
MonotonicityNot monotonic
2023-01-29T13:22:07.372016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 848
28.9%
1 342
 
11.6%
2 203
 
6.9%
3 175
 
6.0%
4 96
 
3.3%
8 57
 
1.9%
7 53
 
1.8%
9 48
 
1.6%
10 48
 
1.6%
6 46
 
1.6%
Other values (199) 1022
34.8%
ValueCountFrequency (%)
0 848
28.9%
1 342
11.6%
2 203
 
6.9%
3 175
 
6.0%
4 96
 
3.3%
ValueCountFrequency (%)
1800 2
0.1%
1700 2
0.1%
1600 1
< 0.1%
1500 2
0.1%
1400 1
< 0.1%

Life expectancy
Real number (ℝ)

Distinct362
Distinct (%)12.4%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean69.22493169
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:07.497181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.4
Q163.1
median72.1
Q375.7
95-th percentile82
Maximum89
Range52.7
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation9.523867488
Coefficient of variation (CV)0.1375785754
Kurtosis-0.2344773942
Mean69.22493169
Median Absolute Deviation (MAD)5.8
Skewness-0.6386047359
Sum202690.6
Variance90.70405193
MonotonicityNot monotonic
2023-01-29T13:22:07.622457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 45
 
1.5%
75 33
 
1.1%
78 31
 
1.1%
73.6 28
 
1.0%
73.9 25
 
0.9%
76 25
 
0.9%
81 25
 
0.9%
74.5 24
 
0.8%
74.7 24
 
0.8%
73.5 23
 
0.8%
Other values (352) 2645
90.0%
ValueCountFrequency (%)
36.3 1
< 0.1%
39 1
< 0.1%
41 1
< 0.1%
41.5 1
< 0.1%
42.3 1
< 0.1%
ValueCountFrequency (%)
89 11
0.4%
88 10
0.3%
87 9
0.3%
86 15
0.5%
85 12
0.4%

Measles
Real number (ℝ)

Distinct958
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2419.59224
Minimum0
Maximum212183
Zeros983
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:07.748504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3360.25
95-th percentile9985.55
Maximum212183
Range212183
Interquartile range (IQR)360.25

Descriptive statistics

Standard deviation11467.27249
Coefficient of variation (CV)4.739340911
Kurtosis114.8599032
Mean2419.59224
Median Absolute Deviation (MAD)17
Skewness9.441331947
Sum7108762
Variance131498338.3
MonotonicityNot monotonic
2023-01-29T13:22:07.864849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 983
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
6 29
 
1.0%
7 28
 
1.0%
5 25
 
0.9%
8 24
 
0.8%
9 22
 
0.7%
Other values (948) 1578
53.7%
ValueCountFrequency (%)
0 983
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
ValueCountFrequency (%)
212183 1
< 0.1%
182485 1
< 0.1%
168107 1
< 0.1%
141258 1
< 0.1%
133802 1
< 0.1%

percentage expenditure
Real number (ℝ)

Distinct2328
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean738.2512955
Minimum0
Maximum19479.91161
Zeros611
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:07.985339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.685342585
median64.91290604
Q3441.5341444
95-th percentile4506.638496
Maximum19479.91161
Range19479.91161
Interquartile range (IQR)436.8488018

Descriptive statistics

Standard deviation1987.914858
Coefficient of variation (CV)2.692734669
Kurtosis26.57338739
Mean738.2512955
Median Absolute Deviation (MAD)64.91290604
Skewness4.652051348
Sum2168982.306
Variance3951805.483
MonotonicityNot monotonic
2023-01-29T13:22:08.108639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 611
 
20.8%
71.27962362 1
 
< 0.1%
3.304039899 1
 
< 0.1%
218.5716179 1
 
< 0.1%
36.81621175 1
 
< 0.1%
2.542436908 1
 
< 0.1%
2.092343893 1
 
< 0.1%
22.35595448 1
 
< 0.1%
15.25518816 1
 
< 0.1%
31.50243237 1
 
< 0.1%
Other values (2318) 2318
78.9%
ValueCountFrequency (%)
0 611
20.8%
0.09987219 1
 
< 0.1%
0.108055973 1
 
< 0.1%
0.27564826 1
 
< 0.1%
0.328418056 1
 
< 0.1%
ValueCountFrequency (%)
19479.91161 1
< 0.1%
19099.04506 1
< 0.1%
18961.3486 1
< 0.1%
18822.86732 1
< 0.1%
18379.32974 1
< 0.1%

Polio
Real number (ℝ)

Distinct73
Distinct (%)2.5%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.55018842
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:08.240984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.42804595
Coefficient of variation (CV)0.2838036641
Kurtosis3.776509819
Mean82.55018842
Median Absolute Deviation (MAD)6
Skewness-2.098053249
Sum240964
Variance548.873337
MonotonicityNot monotonic
2023-01-29T13:22:08.368103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 376
 
12.8%
98 255
 
8.7%
96 207
 
7.0%
97 205
 
7.0%
95 180
 
6.1%
94 159
 
5.4%
93 120
 
4.1%
92 96
 
3.3%
91 88
 
3.0%
9 71
 
2.4%
Other values (63) 1162
39.6%
ValueCountFrequency (%)
3 7
 
0.2%
4 11
0.4%
5 8
 
0.3%
6 11
0.4%
7 24
0.8%
ValueCountFrequency (%)
99 376
12.8%
98 255
8.7%
97 205
7.0%
96 207
7.0%
95 180
6.1%

Population
Real number (ℝ)

Distinct2278
Distinct (%)99.7%
Missing652
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean12753375.12
Minimum34
Maximum1293859294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:08.491696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile9617.5
Q1195793.25
median1386542
Q37420359
95-th percentile47554415.75
Maximum1293859294
Range1293859260
Interquartile range (IQR)7224565.75

Descriptive statistics

Standard deviation61012096.51
Coefficient of variation (CV)4.783996074
Kurtosis298.0102666
Mean12753375.12
Median Absolute Deviation (MAD)1357309.5
Skewness15.9162356
Sum2.915421552 × 1010
Variance3.72247592 × 1015
MonotonicityNot monotonic
2023-01-29T13:22:08.620939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444 4
 
0.1%
718239 2
 
0.1%
1141 2
 
0.1%
26868 2
 
0.1%
127445 2
 
0.1%
292 2
 
0.1%
51448196 1
 
< 0.1%
12262 1
 
< 0.1%
15228525 1
 
< 0.1%
14668338 1
 
< 0.1%
Other values (2268) 2268
77.2%
(Missing) 652
 
22.2%
ValueCountFrequency (%)
34 1
< 0.1%
36 1
< 0.1%
41 1
< 0.1%
43 1
< 0.1%
123 1
< 0.1%
ValueCountFrequency (%)
1293859294 1
< 0.1%
1179681239 1
< 0.1%
1161977719 1
< 0.1%
1144118674 1
< 0.1%
1126135777 1
< 0.1%

Schooling
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct173
Distinct (%)6.2%
Missing163
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean11.99279279
Minimum0
Maximum20.7
Zeros28
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:08.750261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8
Q110.1
median12.3
Q314.3
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.358919721
Coefficient of variation (CV)0.2800781919
Kurtosis0.8861512689
Mean11.99279279
Median Absolute Deviation (MAD)2.1
Skewness-0.6024365419
Sum33280
Variance11.28234169
MonotonicityNot monotonic
2023-01-29T13:22:08.870334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9 58
 
2.0%
13.3 52
 
1.8%
12.5 49
 
1.7%
12.8 46
 
1.6%
12.3 44
 
1.5%
12.6 43
 
1.5%
12.4 42
 
1.4%
10.7 41
 
1.4%
11.9 41
 
1.4%
12.7 40
 
1.4%
Other values (163) 2319
78.9%
(Missing) 163
 
5.5%
ValueCountFrequency (%)
0 28
1.0%
2.8 1
 
< 0.1%
2.9 4
 
0.1%
3 1
 
< 0.1%
3.1 1
 
< 0.1%
ValueCountFrequency (%)
20.7 1
 
< 0.1%
20.6 1
 
< 0.1%
20.5 1
 
< 0.1%
20.4 3
0.1%
20.3 4
0.1%

Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Developing
2426 
Developed
512 

Length

Max length10
Median length10
Mean length9.82573179
Min length9

Characters and Unicode

Total characters28868
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeveloping
2nd rowDeveloping
3rd rowDeveloping
4th rowDeveloping
5th rowDeveloping

Common Values

ValueCountFrequency (%)
Developing 2426
82.6%
Developed 512
 
17.4%

Length

2023-01-29T13:22:08.975662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-29T13:22:09.071498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
developing 2426
82.6%
developed 512
 
17.4%

Most occurring characters

ValueCountFrequency (%)
e 6388
22.1%
D 2938
10.2%
v 2938
10.2%
l 2938
10.2%
o 2938
10.2%
p 2938
10.2%
i 2426
 
8.4%
n 2426
 
8.4%
g 2426
 
8.4%
d 512
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25930
89.8%
Uppercase Letter 2938
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6388
24.6%
v 2938
11.3%
l 2938
11.3%
o 2938
11.3%
p 2938
11.3%
i 2426
 
9.4%
n 2426
 
9.4%
g 2426
 
9.4%
d 512
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
D 2938
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28868
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6388
22.1%
D 2938
10.2%
v 2938
10.2%
l 2938
10.2%
o 2938
10.2%
p 2938
10.2%
i 2426
 
8.4%
n 2426
 
8.4%
g 2426
 
8.4%
d 512
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6388
22.1%
D 2938
10.2%
v 2938
10.2%
l 2938
10.2%
o 2938
10.2%
p 2938
10.2%
i 2426
 
8.4%
n 2426
 
8.4%
g 2426
 
8.4%
d 512
 
1.8%

Total expenditure
Real number (ℝ)

Distinct818
Distinct (%)30.2%
Missing226
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean5.938189528
Minimum0.37
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:09.165331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.93
Q14.26
median5.755
Q37.4925
95-th percentile9.76
Maximum17.6
Range17.23
Interquartile range (IQR)3.2325

Descriptive statistics

Standard deviation2.498319672
Coefficient of variation (CV)0.4207207703
Kurtosis1.156270469
Mean5.938189528
Median Absolute Deviation (MAD)1.59
Skewness0.6186855521
Sum16104.37
Variance6.241601184
MonotonicityNot monotonic
2023-01-29T13:22:09.286015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6 15
 
0.5%
6.7 12
 
0.4%
5.6 11
 
0.4%
9.1 10
 
0.3%
5.64 10
 
0.3%
5.9 10
 
0.3%
5.3 10
 
0.3%
5.25 10
 
0.3%
3.4 10
 
0.3%
4.2 9
 
0.3%
Other values (808) 2605
88.7%
(Missing) 226
 
7.7%
ValueCountFrequency (%)
0.37 1
< 0.1%
0.65 1
< 0.1%
0.74 1
< 0.1%
0.76 1
< 0.1%
0.92 1
< 0.1%
ValueCountFrequency (%)
17.6 1
< 0.1%
17.24 1
< 0.1%
17.2 2
0.1%
17.14 1
< 0.1%
17 1
< 0.1%

under-five deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.0357386
Minimum0
Maximum2500
Zeros785
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:09.413964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q328
95-th percentile138
Maximum2500
Range2500
Interquartile range (IQR)28

Descriptive statistics

Standard deviation160.4455484
Coefficient of variation (CV)3.816884246
Kurtosis109.7527951
Mean42.0357386
Median Absolute Deviation (MAD)4
Skewness9.495064657
Sum123501
Variance25742.774
MonotonicityNot monotonic
2023-01-29T13:22:09.534045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 785
26.7%
1 361
 
12.3%
2 163
 
5.5%
4 161
 
5.5%
3 129
 
4.4%
12 53
 
1.8%
8 49
 
1.7%
6 48
 
1.6%
10 47
 
1.6%
5 44
 
1.5%
Other values (242) 1098
37.4%
ValueCountFrequency (%)
0 785
26.7%
1 361
12.3%
2 163
 
5.5%
3 129
 
4.4%
4 161
 
5.5%
ValueCountFrequency (%)
2500 1
< 0.1%
2400 1
< 0.1%
2300 1
< 0.1%
2200 1
< 0.1%
2100 1
< 0.1%

Year
Real number (ℝ)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.51872
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-29T13:22:09.632926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.61384094
Coefficient of variation (CV)0.002298280406
Kurtosis-1.213721712
Mean2007.51872
Median Absolute Deviation (MAD)4
Skewness-0.006409027359
Sum5898090
Variance21.28752822
MonotonicityNot monotonic
2023-01-29T13:22:09.720241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2013 193
 
6.6%
2015 183
 
6.2%
2014 183
 
6.2%
2012 183
 
6.2%
2011 183
 
6.2%
2010 183
 
6.2%
2009 183
 
6.2%
2008 183
 
6.2%
2007 183
 
6.2%
2006 183
 
6.2%
Other values (6) 1098
37.4%
ValueCountFrequency (%)
2000 183
6.2%
2001 183
6.2%
2002 183
6.2%
2003 183
6.2%
2004 183
6.2%
ValueCountFrequency (%)
2015 183
6.2%
2014 183
6.2%
2013 193
6.6%
2012 183
6.2%
2011 183
6.2%

Interactions

2023-01-29T13:22:01.461442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:22.319715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:24.196546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:26.209491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:28.228940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:30.217806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:32.371114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:34.392635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:36.342071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:38.453349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:40.707593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:42.703515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:44.955372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:46.985896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:48.940277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:51.300111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:53.283439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:55.331196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:21:59.256582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:22:01.554619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:22.446626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:24.283488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:26.298869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:28.320629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:30.327250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:32.464336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:34.480624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:36.433531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:38.542279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:40.822026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:42.797548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:45.062421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:47.076865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:49.048895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:51.390031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:21:55.420116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:57.331355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:59.345928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:22:01.652509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:22.538683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:24.374004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:26.391021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:28.416607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:30.425105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:32.563335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:34.574781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:36.668628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:38.636066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:40.913041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:42.899489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:45.160891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:47.170191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:49.152246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:51.490583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:22:01.751103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:22.626581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:24.553790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:26.485619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:28.513749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:30.524272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:32.662107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:34.668868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:36.763829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:38.730010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:41.006293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:43.177799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:45.267342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:47.265252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:49.256827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:51.585669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:53.578984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:21:57.530957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:59.534270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:22:01.856722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:22.718768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:24.662287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:21:30.628271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:32.766001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:34.768883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:36.861329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:21:41.105540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:43.282133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:45.371391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:47.362723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:49.366262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:21:55.712433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:57.633757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:59.633962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:22:01.962629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:22.822478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:24.761574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:26.684865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:28.717793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:30.732454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:32.873267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:34.871480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:36.963423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:38.926514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:41.206811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:43.390646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:45.483875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:47.466066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:21:51.789119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:53.788342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:55.822238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:57.740239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:59.735935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:21:37.066808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:39.028892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:41.315086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:43.497619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:21:53.179347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-29T13:21:57.136614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:21:59.149684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-29T13:22:01.357971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-29T13:22:10.149993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
YearLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
Year1.0000.170-0.079-0.037-0.0530.0310.104-0.0820.109-0.0430.0940.0910.134-0.1400.1020.017-0.048-0.0510.2430.209
Life expectancy0.1701.000-0.696-0.1970.4050.3820.257-0.1580.568-0.2230.4660.2180.479-0.5570.461-0.022-0.477-0.4720.7250.752
Adult Mortality-0.079-0.6961.0000.079-0.196-0.243-0.1620.031-0.3870.094-0.275-0.115-0.2750.524-0.296-0.0140.3030.308-0.458-0.455
infant deaths-0.037-0.1970.0791.000-0.116-0.086-0.2240.501-0.2270.997-0.171-0.129-0.1750.025-0.1080.5570.4660.471-0.145-0.194
Alcohol-0.0530.405-0.196-0.1161.0000.3410.088-0.0520.330-0.1120.2220.2970.222-0.0490.355-0.035-0.429-0.4170.4500.547
percentage expenditure0.0310.382-0.243-0.0860.3411.0000.016-0.0570.229-0.0880.1470.1740.144-0.0980.899-0.026-0.251-0.2530.3820.390
Hepatitis B0.1040.257-0.162-0.2240.0880.0161.000-0.1210.150-0.2330.4860.0580.611-0.1130.084-0.123-0.120-0.1250.2000.231
Measles-0.082-0.1580.0310.501-0.052-0.057-0.1211.000-0.1760.508-0.136-0.106-0.1420.031-0.0760.2660.2250.221-0.130-0.137
BMI0.1090.568-0.387-0.2270.3300.2290.150-0.1761.000-0.2380.2850.2430.283-0.2440.302-0.072-0.532-0.5390.5090.547
under-five deaths-0.043-0.2230.0940.997-0.112-0.088-0.2330.508-0.2381.000-0.189-0.130-0.1960.038-0.1120.5440.4680.472-0.163-0.209
Polio0.0940.466-0.275-0.1710.2220.1470.486-0.1360.285-0.1891.0000.1370.674-0.1600.212-0.039-0.222-0.2230.3810.418
Total expenditure0.0910.218-0.115-0.1290.2970.1740.058-0.1060.243-0.1300.1371.0000.153-0.0010.138-0.080-0.277-0.2840.1670.246
Diphtheria0.1340.479-0.275-0.1750.2220.1440.611-0.1420.283-0.1960.6740.1531.000-0.1650.201-0.028-0.230-0.2230.4010.425
HIV/AIDS-0.140-0.5570.5240.025-0.049-0.098-0.1130.031-0.2440.038-0.160-0.001-0.1651.000-0.136-0.0280.2040.207-0.250-0.220
GDP0.1020.461-0.296-0.1080.3550.8990.084-0.0760.302-0.1120.2120.1380.201-0.1361.000-0.028-0.286-0.2910.4600.448
Population0.017-0.022-0.0140.557-0.035-0.026-0.1230.266-0.0720.544-0.039-0.080-0.028-0.028-0.0281.0000.2540.251-0.009-0.032
thinness 1-19 years-0.048-0.4770.3030.466-0.429-0.251-0.1200.225-0.5320.468-0.222-0.277-0.2300.204-0.2860.2541.0000.939-0.422-0.472
thinness 5-9 years-0.051-0.4720.3080.471-0.417-0.253-0.1250.221-0.5390.472-0.223-0.284-0.2230.207-0.2910.2510.9391.000-0.411-0.461
Income composition of resources0.2430.725-0.458-0.1450.4500.3820.200-0.1300.509-0.1630.3810.1670.401-0.2500.460-0.009-0.422-0.4111.0000.800
Schooling0.2090.752-0.455-0.1940.5470.3900.231-0.1370.547-0.2090.4180.2460.425-0.2200.448-0.032-0.472-0.4610.8001.000
2023-01-29T13:22:10.365703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
YearLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
Year1.0000.157-0.053-0.052-0.101-0.0500.099-0.0950.149-0.0520.1090.0800.132-0.0560.1810.045-0.041-0.0390.2020.195
Life expectancy0.1571.000-0.650-0.6010.4430.4290.350-0.2810.585-0.6190.5350.2940.545-0.7540.642-0.090-0.611-0.6210.8660.814
Adult Mortality-0.053-0.6501.0000.392-0.217-0.297-0.2270.146-0.3930.405-0.319-0.175-0.3280.523-0.3830.0970.3890.404-0.548-0.496
infant deaths-0.052-0.6010.3921.000-0.382-0.361-0.3430.573-0.4800.993-0.430-0.218-0.4260.487-0.5080.4500.4530.468-0.578-0.598
Alcohol-0.1010.443-0.217-0.3821.0000.3030.114-0.1980.324-0.3810.2610.3390.277-0.1970.427-0.009-0.466-0.4600.5140.551
percentage expenditure-0.0500.429-0.297-0.3610.3031.0000.102-0.1530.279-0.3620.2100.1650.224-0.2550.807-0.065-0.305-0.3060.5060.489
Hepatitis B0.0990.350-0.227-0.3430.1140.1021.000-0.2230.195-0.3430.7930.0450.817-0.3370.256-0.115-0.045-0.0620.3560.358
Measles-0.095-0.2810.1460.573-0.198-0.153-0.2231.000-0.2770.574-0.268-0.187-0.2660.204-0.2140.2960.3110.325-0.229-0.282
BMI0.1490.585-0.393-0.4800.3240.2790.195-0.2771.000-0.4910.3250.2670.335-0.5180.481-0.068-0.564-0.5740.6180.615
under-five deaths-0.052-0.6190.4050.993-0.381-0.362-0.3430.574-0.4911.000-0.434-0.223-0.4290.512-0.5140.4430.4610.474-0.589-0.609
Polio0.1090.535-0.319-0.4300.2610.2100.793-0.2680.325-0.4341.0000.1420.921-0.4850.395-0.098-0.220-0.2300.5270.524
Total expenditure0.0800.294-0.175-0.2180.3390.1650.045-0.1870.267-0.2230.1421.0000.158-0.1430.156-0.093-0.360-0.3760.2200.290
Diphtheria0.1320.545-0.328-0.4260.2770.2240.817-0.2660.335-0.4290.9210.1581.000-0.4710.403-0.088-0.233-0.2420.5320.529
HIV/AIDS-0.056-0.7540.5230.487-0.197-0.255-0.3370.204-0.5180.512-0.485-0.143-0.4711.000-0.4790.0930.4760.463-0.649-0.618
GDP0.1810.642-0.383-0.5080.4270.8070.256-0.2140.481-0.5140.3950.1560.403-0.4791.000-0.049-0.419-0.4280.6950.665
Population0.045-0.0900.0970.450-0.009-0.065-0.1150.296-0.0680.443-0.098-0.093-0.0880.093-0.0491.0000.0770.089-0.055-0.070
thinness 1-19 years-0.041-0.6110.3890.453-0.466-0.305-0.0450.311-0.5640.461-0.220-0.360-0.2330.476-0.4190.0771.0000.947-0.577-0.576
thinness 5-9 years-0.039-0.6210.4040.468-0.460-0.306-0.0620.325-0.5740.474-0.230-0.376-0.2420.463-0.4280.0890.9471.000-0.576-0.577
Income composition of resources0.2020.866-0.548-0.5780.5140.5060.356-0.2290.618-0.5890.5270.2200.532-0.6490.695-0.055-0.577-0.5761.0000.901
Schooling0.1950.814-0.496-0.5980.5510.4890.358-0.2820.615-0.6090.5240.2900.529-0.6180.665-0.070-0.576-0.5770.9011.000
2023-01-29T13:22:10.573261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
YearLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
Year1.0000.111-0.037-0.038-0.069-0.0300.070-0.0680.105-0.0370.0770.0550.094-0.0430.1250.032-0.029-0.0280.1410.136
Life expectancy0.1111.000-0.558-0.4280.2950.3110.236-0.1850.428-0.4410.3700.2090.379-0.6070.466-0.059-0.443-0.4510.6930.616
Adult Mortality-0.037-0.5581.0000.279-0.143-0.208-0.1540.095-0.2830.289-0.220-0.122-0.2280.435-0.2730.0630.2730.282-0.425-0.368
infant deaths-0.038-0.4280.2791.000-0.276-0.260-0.2500.434-0.3490.970-0.311-0.154-0.3110.381-0.3610.3200.3280.338-0.422-0.435
Alcohol-0.0690.295-0.143-0.2761.0000.2140.081-0.1360.228-0.2730.1840.2340.196-0.1430.297-0.005-0.322-0.3170.3590.388
percentage expenditure-0.0300.311-0.208-0.2600.2141.0000.073-0.1050.203-0.2610.1470.1140.159-0.1930.688-0.042-0.217-0.2180.3660.347
Hepatitis B0.0700.236-0.154-0.2500.0810.0731.000-0.1590.132-0.2490.6700.0310.721-0.2590.175-0.079-0.027-0.0380.2420.246
Measles-0.068-0.1850.0950.434-0.136-0.105-0.1591.000-0.1900.434-0.193-0.125-0.1890.153-0.1450.2090.2190.229-0.148-0.191
BMI0.1050.428-0.283-0.3490.2280.2030.132-0.1901.000-0.3570.2230.1910.231-0.3940.340-0.043-0.428-0.4360.4620.452
under-five deaths-0.037-0.4410.2890.970-0.273-0.261-0.2490.434-0.3571.000-0.312-0.158-0.3120.399-0.3650.3140.3330.342-0.434-0.445
Polio0.0770.370-0.220-0.3110.1840.1470.670-0.1930.223-0.3121.0000.0990.830-0.3710.269-0.066-0.146-0.1530.3640.363
Total expenditure0.0550.209-0.122-0.1540.2340.1140.031-0.1250.191-0.1580.0991.0000.110-0.1090.108-0.061-0.256-0.2690.1570.202
Diphtheria0.0940.379-0.228-0.3110.1960.1590.721-0.1890.231-0.3120.8300.1101.000-0.3610.277-0.060-0.156-0.1620.3720.369
HIV/AIDS-0.043-0.6070.4350.381-0.143-0.193-0.2590.153-0.3940.399-0.371-0.109-0.3611.000-0.3600.0690.3560.346-0.503-0.470
GDP0.1250.466-0.273-0.3610.2970.6880.175-0.1450.340-0.3650.2690.1080.277-0.3601.000-0.032-0.290-0.2960.5220.481
Population0.032-0.0590.0630.320-0.005-0.042-0.0790.209-0.0430.314-0.066-0.061-0.0600.069-0.0321.0000.0530.061-0.033-0.046
thinness 1-19 years-0.029-0.4430.2730.328-0.322-0.217-0.0270.219-0.4280.333-0.146-0.256-0.1560.356-0.2900.0531.0000.930-0.415-0.423
thinness 5-9 years-0.028-0.4510.2820.338-0.317-0.218-0.0380.229-0.4360.342-0.153-0.269-0.1620.346-0.2960.0610.9301.000-0.414-0.422
Income composition of resources0.1410.693-0.425-0.4220.3590.3660.242-0.1480.462-0.4340.3640.1570.372-0.5030.522-0.033-0.415-0.4141.0000.745
Schooling0.1360.616-0.368-0.4350.3880.3470.246-0.1910.452-0.4450.3630.2020.369-0.4700.481-0.046-0.423-0.4220.7451.000
2023-01-29T13:22:10.786940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
YearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
Year1.0000.0000.2080.0000.0180.1560.0830.0580.0540.3110.0000.1180.1040.1230.0960.0890.0000.0000.0030.1790.182
Status0.0001.0000.7930.4760.0650.8350.5800.2240.0280.5940.0780.3970.5600.4080.1650.4780.0810.5990.4650.6960.811
Life expectancy0.2080.7931.0000.8610.2170.5390.5530.3470.1680.5530.2770.6230.5610.6350.6680.4400.1060.6540.5200.7330.771
Adult Mortality0.0000.4760.8611.0000.1240.3300.3060.3730.0990.4470.2060.5160.3570.5430.7920.2560.0570.5190.4020.5530.627
infant deaths0.0180.0650.2170.1241.0000.1490.0000.2980.6090.2080.9330.2940.1610.3350.0910.0000.8190.6880.8330.3090.190
Alcohol0.1560.8350.5390.3300.1491.0000.4030.2780.0000.5160.2040.2610.5040.3590.1910.3580.0000.4890.3750.5060.632
percentage expenditure0.0830.5800.5530.3060.0000.4031.0000.1670.0000.3100.0000.1310.4620.1340.0000.8390.0000.2440.1900.4800.514
Hepatitis B0.0580.2240.3470.3730.2980.2780.1671.0000.1700.3840.3600.8520.2560.9220.2470.1530.1490.2740.2810.3540.347
Measles0.0540.0280.1680.0990.6090.0000.0000.1701.0000.2470.7350.4270.0000.3940.0000.0000.4060.4590.3460.2740.141
BMI0.3110.5940.5530.4470.2080.5160.3100.3840.2471.0000.2680.5190.5350.5130.3840.2960.0970.6820.5470.6000.723
under-five deaths0.0000.0780.2770.2060.9330.2040.0000.3600.7350.2681.0000.4240.1980.4240.0000.0000.7530.7950.6860.1880.306
Polio0.1180.3970.6230.5160.2940.2610.1310.8520.4270.5190.4241.0000.1900.9470.3420.1850.1190.4690.3360.4990.603
Total expenditure0.1040.5600.5610.3570.1610.5040.4620.2560.0000.5350.1980.1901.0000.2600.1080.3020.1470.4550.3540.5470.583
Diphtheria0.1230.4080.6350.5430.3350.3590.1340.9220.3940.5130.4240.9470.2601.0000.3340.1920.1520.4630.3430.5020.601
HIV/AIDS0.0960.1650.6680.7920.0910.1910.0000.2470.0000.3840.0000.3420.1080.3341.0000.0000.0000.3860.2900.3070.311
GDP0.0890.4780.4400.2560.0000.3580.8390.1530.0000.2960.0000.1850.3020.1920.0001.0000.0000.1940.2850.6770.420
Population0.0000.0810.1060.0570.8190.0000.0000.1490.4060.0970.7530.1190.1470.1520.0000.0001.0000.5360.4840.0960.059
thinness 1-19 years0.0000.5990.6540.5190.6880.4890.2440.2740.4590.6820.7950.4690.4550.4630.3860.1940.5361.0000.9600.5230.657
thinness 5-9 years0.0030.4650.5200.4020.8330.3750.1900.2810.3460.5470.6860.3360.3540.3430.2900.2850.4840.9601.0000.6620.500
Income composition of resources0.1790.6960.7330.5530.3090.5060.4800.3540.2740.6000.1880.4990.5470.5020.3070.6770.0960.5230.6621.0000.789
Schooling0.1820.8110.7710.6270.1900.6320.5140.3470.1410.7230.3060.6030.5830.6010.3110.4200.0590.6570.5000.7891.000
2023-01-29T13:22:11.000319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
BMIHIV/AIDSthinness 1-19 yearsthinness 5-9 yearsAdult MortalityAlcoholDiphtheriaGDPHepatitis BIncome composition of resourcesinfant deathsLife expectancyMeaslespercentage expenditurePolioPopulationSchoolingTotal expenditureunder-five deathsYearStatus
BMI1.000-0.518-0.564-0.574-0.3930.3240.3350.4810.1950.618-0.4800.585-0.2770.2790.325-0.0680.6150.267-0.4910.1490.459
HIV/AIDS-0.5181.0000.4760.4630.523-0.197-0.471-0.479-0.337-0.6490.487-0.7540.204-0.255-0.4850.093-0.618-0.1430.512-0.0560.126
thinness 1-19 years-0.5640.4761.0000.9470.389-0.466-0.233-0.419-0.045-0.5770.453-0.6110.311-0.305-0.2200.077-0.576-0.3600.461-0.0410.462
thinness 5-9 years-0.5740.4630.9471.0000.404-0.460-0.242-0.428-0.062-0.5760.468-0.6210.325-0.306-0.2300.089-0.577-0.3760.474-0.0390.466
Adult Mortality-0.3930.5230.3890.4041.000-0.217-0.328-0.383-0.227-0.5480.392-0.6500.146-0.297-0.3190.097-0.496-0.1750.405-0.0530.366
Alcohol0.324-0.197-0.466-0.460-0.2171.0000.2770.4270.1140.514-0.3820.443-0.1980.3030.261-0.0090.5510.339-0.381-0.1010.667
Diphtheria0.335-0.471-0.233-0.242-0.3280.2771.0000.4030.8170.532-0.4260.545-0.2660.2240.921-0.0880.5290.158-0.4290.1320.313
GDP0.481-0.479-0.419-0.428-0.3830.4270.4031.0000.2560.695-0.5080.642-0.2140.8070.395-0.0490.6650.156-0.5140.1810.478
Hepatitis B0.195-0.337-0.045-0.062-0.2270.1140.8170.2561.0000.356-0.3430.350-0.2230.1020.793-0.1150.3580.045-0.3430.0990.172
Income composition of resources0.618-0.649-0.577-0.576-0.5480.5140.5320.6950.3561.000-0.5780.866-0.2290.5060.527-0.0550.9010.220-0.5890.2020.706
infant deaths-0.4800.4870.4530.4680.392-0.382-0.426-0.508-0.343-0.5781.000-0.6010.573-0.361-0.4300.450-0.598-0.2180.993-0.0520.065
Life expectancy0.585-0.754-0.611-0.621-0.6500.4430.5450.6420.3500.866-0.6011.000-0.2810.4290.535-0.0900.8140.294-0.6190.1570.627
Measles-0.2770.2040.3110.3250.146-0.198-0.266-0.214-0.223-0.2290.573-0.2811.000-0.153-0.2680.296-0.282-0.1870.574-0.0950.022
percentage expenditure0.279-0.255-0.305-0.306-0.2970.3030.2240.8070.1020.506-0.3610.429-0.1531.0000.210-0.0650.4890.165-0.362-0.0500.448
Polio0.325-0.485-0.220-0.230-0.3190.2610.9210.3950.7930.527-0.4300.535-0.2680.2101.000-0.0980.5240.142-0.4340.1090.304
Population-0.0680.0930.0770.0890.097-0.009-0.088-0.049-0.115-0.0550.450-0.0900.296-0.065-0.0981.000-0.070-0.0930.4430.0450.053
Schooling0.615-0.618-0.576-0.577-0.4960.5510.5290.6650.3580.901-0.5980.814-0.2820.4890.524-0.0701.0000.290-0.6090.1950.643
Total expenditure0.267-0.143-0.360-0.376-0.1750.3390.1580.1560.0450.220-0.2180.294-0.1870.1650.142-0.0930.2901.000-0.2230.0800.431
under-five deaths-0.4910.5120.4610.4740.405-0.381-0.429-0.514-0.343-0.5890.993-0.6190.574-0.362-0.4340.443-0.609-0.2231.000-0.0520.060
Year0.149-0.056-0.041-0.039-0.053-0.1010.1320.1810.0990.202-0.0520.157-0.095-0.0500.1090.0450.1950.080-0.0521.0000.000
Status0.4590.1260.4620.4660.3660.6670.3130.4780.1720.7060.0650.6270.0220.4480.3040.0530.6430.4310.0600.0001.000

Missing values

2023-01-29T13:22:03.557190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-29T13:22:03.859107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-29T13:22:04.099010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryYearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
0Afghanistan2015Developing65.0263.0620.0171.27962465.0115419.1836.08.1665.00.1584.25921033736494.017.217.30.47910.1
1Afghanistan2014Developing59.9271.0640.0173.52358262.049218.68658.08.1862.00.1612.696514327582.017.517.50.47610.0
2Afghanistan2013Developing59.9268.0660.0173.21924364.043018.18962.08.1364.00.1631.74497631731688.017.717.70.4709.9
3Afghanistan2012Developing59.5272.0690.0178.18421567.0278717.69367.08.5267.00.1669.9590003696958.017.918.00.4639.8
4Afghanistan2011Developing59.2275.0710.017.09710968.0301317.29768.07.8768.00.163.5372312978599.018.218.20.4549.5
5Afghanistan2010Developing58.8279.0740.0179.67936766.0198916.710266.09.2066.00.1553.3289402883167.018.418.40.4489.2
6Afghanistan2009Developing58.6281.0770.0156.76221763.0286116.210663.09.4263.00.1445.893298284331.018.618.70.4348.9
7Afghanistan2008Developing58.1287.0800.0325.87392564.0159915.711064.08.3364.00.1373.3611162729431.018.818.90.4338.7
8Afghanistan2007Developing57.5295.0820.0210.91015663.0114115.211363.06.7363.00.1369.83579626616792.019.019.10.4158.4
9Afghanistan2006Developing57.3295.0840.0317.17151864.0199014.711658.07.4358.00.1272.5637702589345.019.219.30.4058.1
CountryYearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
2928Zimbabwe2009Developing50.0587.0304.641.04002173.085329.04569.06.2673.018.165.8241211381599.07.57.40.4199.9
2929Zimbabwe2008Developing48.2632.0303.5620.84342975.0028.64675.04.9675.020.5325.67857313558469.07.87.80.4219.7
2930Zimbabwe2007Developing46.667.0293.8829.81456672.024228.24673.04.4773.023.7396.9982171332999.08.28.20.4149.6
2931Zimbabwe2006Developing45.47.0284.5734.26216968.021227.94571.05.127.026.8414.79623213124267.08.68.60.4089.5
2932Zimbabwe2005Developing44.6717.0284.148.71740965.042027.54369.06.4468.030.3444.765750129432.09.09.00.4069.3
2933Zimbabwe2004Developing44.3723.0274.360.00000068.03127.14267.07.1365.033.6454.36665412777511.09.49.40.4079.2
2934Zimbabwe2003Developing44.5715.0264.060.0000007.099826.7417.06.5268.036.7453.35115512633897.09.89.90.4189.5
2935Zimbabwe2002Developing44.873.0254.430.00000073.030426.34073.06.5371.039.857.348340125525.01.21.30.42710.0
2936Zimbabwe2001Developing45.3686.0251.720.00000076.052925.93976.06.1675.042.1548.58731212366165.01.61.70.4279.8
2937Zimbabwe2000Developing46.0665.0241.680.00000079.0148325.53978.07.1078.043.5547.35887812222251.011.011.20.4349.8
CountryYearStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
344Botswana2007Developing56.9436.026.21512.58880093.0133.7396.04.7196.013.45714.4793701914414.09.29.00.63012.1
1058Guatemala2015Developing71.9186.010NaN0.00000074.005.6129.0NaN74.00.43923.57334416252429.01.21.20.63710.7
1211Indonesia2006Developing67.3191.01590.0672.01593266.02042219.719478.02.9172.00.11586.25400022983822.01.81.80.63210.9
1163Hungary2006Developed73.4177.0113.161299.459306NaN159.3199.08.1099.00.111398.76584017137.02.02.00.80215.2
2244Saudi Arabia2004Developing73.112.090.06816.47950896.0188059.41196.03.5896.00.111138.874600NaN7.27.30.75412.3